{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LplM9PSe8djM"
      },
      "source": [
        "# Summarizing meeting notes\n",
        "\n",
        "Welcome to Cohere! In this notebook, you'll learn how to:\n",
        "* Use Cohere's Command-R model to summarize meeting transcripts.\n",
        "* Modify your prompt to include specific formatting instructions, especially if the model output is used in downstream applications.\n",
        "* Use Command-R with LangChain for summarization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SfEhd3O74--"
      },
      "source": [
        "## Setup\n",
        "\n",
        "You'll need a Cohere API key to run this notebook. If you don't have a key, head to https://cohere.com/ to generate your key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "H2TfKiPM3a6f"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "# TODO: upgrade to \"cohere>5\"",
"!pip install \"cohere<5\" datasets tokenizers langchain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1HzGqnY74bj",
        "outputId": "24fba7c5-a2ce-4c86-bc14-4ca2fb9137b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your Cohere API key: ··········\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "from string import Template\n",
        "from typing import Optional\n",
        "\n",
        "import cohere\n",
        "from getpass import getpass\n",
        "from datasets import load_dataset\n",
        "\n",
        "# Set up Cohere client\n",
        "co_api_key = getpass(\"Enter your Cohere API key: \")\n",
        "co_model = \"command-r\"\n",
        "co = cohere.Client(api_key=co_api_key)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "SbVCPxMSD3fs"
      },
      "outputs": [],
      "source": [
        "# We'll also defining some util functions for later\n",
        "\n",
        "def pprint(s: Optional[str] = None, maxchars: int = 100):\n",
        "  \"\"\"\n",
        "  Wrap long text into lines of at most `maxchars` (preserves linebreaks occurring in text)\n",
        "  \"\"\"\n",
        "  if not s:\n",
        "    print()\n",
        "  else:\n",
        "    print(\"\\n\".join(line.strip() for line in re.findall(rf\".{{1,{maxchars}}}(?:\\s+|$)\", s)))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABgVdI7I8Jmk"
      },
      "source": [
        "## Load test data\n",
        "\n",
        "Let's load a meeting transcript to see Command in action!\n",
        "\n",
        "* If you have your own transcript, you can load it to Colab using your favorite method.\n",
        "* If you don't, we'll use a sample from the [QMSum dataset](https://github.com/Yale-LILY/QMSum). QMSum contains cleaned meeting transcripts with [diarised speakers](https://en.wikipedia.org/wiki/Speaker_diarisation).\n",
        "* We'll see later that the recipe shared herein isn't limited to meeting notes transcript, but extends to any data with diarised speakers!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        },
        "id": "yS-hEiwQ71lv",
        "outputId": "30c66020-c5b0-4984-fce6-9ae018b7abf0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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            "text/plain": [
              "Downloading readme:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
            ]
          },
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              "Generating train split: 0 examples [00:00, ? examples/s]"
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              "Generating validation split: 0 examples [00:00, ? examples/s]"
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              "Generating test split: 0 examples [00:00, ? examples/s]"
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PhD D: Sure because I I need a lot of time to to put the label or to do that\n",
            "Professor E: I mean we we know that there s noise There s there s continual noise from fans and so\n",
            "forth and there is more impulsive noise from taps and so forth and and something in between with\n",
            "paper rustling We know that all that s there and it s a g worthwhile thing to study but obviously it\n",
            "takes a lot of time to mark all of these things Whereas th i I would think that you we can study\n",
            "more or less as a distinct phenomenon the overlapping of people talking So Then you can get the Cuz\n",
            "you need If it s three hundred i i it sounds like you probably only have fifty or sixty or seventy\n",
            "events right now that are really And and you need to have a lot more than that to have any kind of\n",
            "even visual sense of of what s going on much less any kind of reasonable statistics\n",
            "PhD C: Now why do you need to mark speaker overlap by hand if you can infer it from the relative\n",
            "energy in the\n",
            "Grad G: Well that s That s what I was going to bring up\n",
            "PhD C: I mean you should not need to do this p completely by hand\n",
            "Professor E: OK So let s back up because you were not here for an earlier conversation\n",
            "PhD C: right ? I m sorry\n",
            "Professor E: So the idea was that what he was going to be doing was experimenting with different\n",
            "measures such as the increase in energy such as the energy in the LPC residuals such as I mean there\n",
            "s a bunch of things I mean increased energy is is sort of an obvious one\n",
            "PhD C: Mm In the far field mike\n",
            "Professor E: and it s not obvious I mean you could you could do the dumbest thing and get get it\n",
            "ninety percent of the time But when you start going past that and trying to do better it s not\n",
            "obvious what combination of features is going to give you the you know the right detector So the\n",
            "idea is to have some ground truth first And so the i the idea of the manual marking was to say `` OK\n",
            "this i you know it s it s really here ``\n",
            "PhD A: But I think Liz is saying why not get it out of the transcripts ?\n",
            "PhD C: What I mean is pause get it from the close talking mikes A or ge get a first pass from those\n",
            "Professor E: We t we t w we t we talked about that\n",
            "PhD C: and then go through sort of It would be a lot faster probably to\n",
            "Professor E: We we we talked about that s But so it s a bootstrapping thing and the thing is the\n",
            "idea was i we i i we thought it would be useful for him to look at the data anyway and and then\n",
            "whatever he could mark would be helpful and we could it s a question of what you bootstrap from You\n",
            "know do you bootstrap from a simple measurement which is right most of the time and then you g do\n",
            "better or do you bootstrap from some human being looking at it and then then do your simple\n",
            "measurements from the close talking mike I mean even with the close talking mike you are not going\n",
            "to get it right all the time\n",
            "PhD C: Well that s what I wonder because or how bad it is be because that would be interesting\n",
            "Grad G: I m working on a program to do that and\n",
            "PhD C: especially because the bottleneck is the transcription Right ? I mean we ve got a lot more\n",
            "data than we have transcriptions for We have the audio data we have the close talking mike so I mean\n",
            "it seems like one kind of project that s not perfect but that you can get the training data for\n",
            "pretty quickly is you know if you infer form the close talking mikes where the on off points are of\n",
            "speech you know how can we detect that from a far field ?\n",
            "Grad G: I ve I ve written a program to do that\n",
            "PhD C: OK I m sorry I missed the\n",
            "Grad G: and so but it s it s doing something very very simple It just takes a threshold based on on\n",
            "the volume\n",
            "PhD F: Or you can set the threshold low and then weed out the false alarms by hand\n",
            "Grad G: and then it does a median filter and then it looks for runs And it seems to work I ve I m\n",
            "sort of fiddling with the parameters to get it to actually generate something and I have not I do\n",
            "not what I m working on was working on was getting it to a form where we can import it into the user\n",
            "interface that we have pause into Transcriber And so I told I said it would take about a day I ve\n",
            "worked on it for about half a day so give me another half day and I we will have something we can\n",
            "play with\n",
            "Professor E: See this is where we really need the Meeting Recorder query stuff to be working because\n",
            "we ve had these meetings and we ve had this discussion about this and I m sort of remembering a\n",
            "little bit about what we decided\n",
            "PhD C: Right I m sorry I just\n",
            "Professor E: but I could not remember all of it So I think it was partly that you know give somebody\n",
            "a chance to actually look at the data and see what these are like partly that we have e some ground\n",
            "truth to compare against you know when when he he gets his thing going\n",
            "PhD C: Well it s definitely good to have somebody look at it I was just thinking as a way to speed\n",
            "up you know the amount of\n",
            "Professor E: That was that was exactly the notion that that that we discussed\n"
          ]
        }
      ],
      "source": [
        "# If you have your own transcript you would like to test Command on, load it here\n",
        "# transcript = ...\n",
        "\n",
        "# Otherwise, we'll use QMSum\n",
        "# Note this will download the QMSum dataset to your instance\n",
        "qmsum = load_dataset(\"MocktaiLEngineer/qmsum-processed\")\n",
        "# Pick any one transcript\n",
        "transcripts = qmsum[\"validation\"][\"meeting_transcript\"]\n",
        "transcript = transcripts[0]\n",
        "pprint(transcript)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TVWdxs3IIqpr"
      },
      "source": [
        "Let's also check how many tokens that transcript corresponds to."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGlS8mMX5Kdw",
        "outputId": "844aa95f-8a81-48e0-c8fd-6dea58bd33c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of tokens: 1152\n"
          ]
        }
      ],
      "source": [
        "# Measure the number of tokens\n",
        "num_tokens = len(co.tokenize(transcript, model=co_model).tokens)\n",
        "pprint(f\"Number of tokens: {num_tokens}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSMTIfnH8xSk"
      },
      "source": [
        "## Simple summarization\n",
        "\n",
        "First, we'll show an example of a simple summarization task. The prompt template we are using is shown below, and we can obtain the completion using a one line call to the Cohere API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAFIK0OP8vwU",
        "outputId": "37300916-0f32-4826-9bcf-608142ce0676"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The team discusses the time-consuming process of identifying instances of speaker overlap in audio\n",
            "recordings. PhD C suggests using close-talking microphones and automated methods to establish ground\n",
            "truth data, which Grad G is working on. This data would help develop a more efficient system than\n",
            "manual marking.\n"
          ]
        }
      ],
      "source": [
        "prompt_template = Template(\"\"\"## meeting transcript\n",
        "$transcript\n",
        "\n",
        "## instructions\n",
        "Generate a summary of the above meeting transcript. \\\n",
        "Don't include preambles, postambles or explanations. \\\n",
        "\"\"\")\n",
        "prompt = prompt_template.substitute(transcript=transcript)\n",
        "\n",
        "# Use the Cohere API to get the response\n",
        "resp = co.chat(message=prompt, model=co_model, temperature=0.2).text\n",
        "pprint(resp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PII_STgJ6oz"
      },
      "source": [
        "We applied a few best practices in constructing this prompt:\n",
        "\n",
        "1. We include a preamble imbuing the model with a persona.\n",
        "2. We use Markdown-style headers (i.e. with `##`) to delineate the preamble, the text-to-summarise, the instructions, and the output\n",
        "\n",
        "For some other prompting best practices and tips/tricks, see our [prompting guide](https://docs.google.com/document/d/1k0KBiOUEPq65vqEuAQyg6Y5l1s2_Ybwv-lOLho6NHsw/edit)!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YT6nsN6DK_xt"
      },
      "source": [
        "## More advanced formatting\n",
        "\n",
        "That worked well, but what if you want to have more control over the summary? What if you wanted a bulleted summary and wanted exactly 3 bullets in a json structure?\n",
        "\n",
        "Let's try adding in some more specific formatting instructions.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBw5GnX7K_xt",
        "outputId": "7bba22f3-3618-4e62-b010-8c55b96188b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "```json\n",
            "{\n",
            "    \"summary_bullets\": [\n",
            "        \"There's a need to identify instances of speaker overlap in recorded meetings, but it's a time-consuming task to mark these events manually.\",\n",
            "        \"An alternative is to infer speaker overlap using various measurements of energy in the audio, such as close-talking microphones, LPC residuals, etc.\",\n",
            "        \"A threshold-based program that detects speech based on volume can speed up the process, and further improvements can be explored once a robust system is in place.\"\n",
            "    ]\n",
            "}\n",
            "```\n"
          ]
        }
      ],
      "source": [
        "prompt_template = Template(\"\"\"## meeting transcript\n",
        "$transcript\n",
        "\n",
        "## instructions\n",
        "Generate a summary of the above meeting transcript in 3 bullets. \\\n",
        "Use the following JSON format:\n",
        "{\n",
        "    \"summary_bullets\": [\n",
        "        \"<bullet 1>\",\n",
        "        \"<bullet 2>\",\n",
        "        ...\n",
        "    ]\n",
        "}\n",
        "Don't include preambles, postambles or explanations.\\\n",
        "\"\"\")\n",
        "prompt = prompt_template.substitute(transcript=transcript)\n",
        "\n",
        "# Use the Cohere API to get the response\n",
        "resp = co.chat(message=prompt, model=co_model, temperature=0.2).text\n",
        "print(resp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inBjpwOK6pTN"
      },
      "source": [
        "That worked well! Asking for a structured response like a JSON object is a great approach for use cases where you will need to parse or post-process the output for use in downstream applications. To ensure the best possible completion, I provided a template of the expected JSON that the model filled out.\n",
        "\n",
        "Let's also try asking for a short 1 sentence summary."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5aC_E4fK_xt",
        "outputId": "aeefd38d-4673-443b-f959-f500e281b764"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The team discusses using various methods, including close-talking mics and threshold-based volume\n",
            "filters, to accurately identify instances of speaker overlap in recorded meetings.\n"
          ]
        }
      ],
      "source": [
        "prompt_template = Template(\"\"\"## meeting transcript\n",
        "$transcript\n",
        "\n",
        "## instructions\n",
        "Generate a summary of the above meeting transcript in 1 concise sentence. Make sure the sentence is extremely short.\\\n",
        "Don't include preambles, postambles or explanations.\\\n",
        "\"\"\")\n",
        "prompt = prompt_template.substitute(transcript=transcript)\n",
        "\n",
        "# Use the Cohere API to get the response\n",
        "resp = co.chat(message=prompt, model=co_model, temperature=0.2).text\n",
        "pprint(resp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KOaKiT6Ztkbz"
      },
      "source": [
        "## LangChain summarization\n",
        "It's also very easy to use Command-R with LangChain for summarization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5eJM29wWtx-m",
        "outputId": "3f29b633-e406-4608-95d3-60653ff41c51"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\u001b[1m> Entering new StuffDocumentsChain chain...\u001b[0m\n",
            "\n",
            "\u001b[1m> Finished chain.\u001b[0m\n",
            "The team discusses the time-consuming process of identifying instances of speaker overlap in audio\n",
            "recordings. PhD C suggests using close-talking microphones and automated methods to establish ground\n",
            "truth data, which Grad G is working on. This data would help develop a more efficient system than\n",
            "manual marking.\n"
          ]
        }
      ],
      "source": [
        "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
        "from langchain.chains.llm import LLMChain\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.docstore.document import Document\n",
        "from langchain_community.chat_models import ChatCohere\n",
        "\n",
        "llm = ChatCohere(\n",
        "    cohere_api_key=co_api_key,\n",
        "    model=\"command-r\",\n",
        "    temperature=0.2,\n",
        "  )\n",
        "\n",
        "# Define prompt\n",
        "prompt_template = \"\"\"## meeting transcript\n",
        "{transcript}\n",
        "\n",
        "## instructions\n",
        "Generate a summary of the above meeting transcript. \\\n",
        "Don't include preambles, postambles or explanations.\"\"\"\n",
        "prompt = PromptTemplate.from_template(prompt_template)\n",
        "\n",
        "# Define LLM chain\n",
        "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
        "\n",
        "# Define StuffDocumentsChain\n",
        "stuff_chain = StuffDocumentsChain(llm_chain=llm_chain, document_variable_name=\"transcript\", verbose=True)\n",
        "\n",
        "# Load documents\n",
        "docs = [Document(page_content=transcript)]\n",
        "\n",
        "pprint(stuff_chain.run(docs))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtS7tZi9t-dY"
      },
      "source": [
        "Command-R has a context window of 128K tokens, so you can use very long documents, or multiple documents, all in a single API call.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uj8kdy5auKKY"
      },
      "source": [
        "## Wrap up\n",
        "\n",
        "Those were some simple examples of how you can use Cohere's Command-R model for summarization.\n",
        "\n",
        "For more advanced summarization objectives and to see some other cool things you can do with the Command-R model, see [recipes for better meeting notes summaries](https://colab.research.google.com/drive/1XqRpJH7qRnRTbOEt6kthwqZG6gtEn4gN#scrollTo=LplM9PSe8djM).\n",
        "\n",
        "In cases where you can't fit the input text into the context window, see our [cookbook for long document strategies](https://colab.research.google.com/drive/1zxSAbruOWwWJHNsj3N56uxZtUeiS7Evd#scrollTo=4JgYYTg7Shvq).\n",
        "\n",
        "For more information, you can reach out to us at summarize@cohere.com!"
      ]
    }
  ],
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